Pediatric Pain Management Education in Medical Students: Impact of a Web-Based Module
Bibliographic record
Abstract
OBJECTIVE: Reports from the Institute of Medicine and American Medical Association's Pain and Palliative Medicine Specialty Section Council emphasize the need for pain management education in medical schools, yet training in pediatric pain management (PPM) is limited. In this pilot program, we evaluated the effectiveness of a web-based PPM module on knowledge, confidence, attitudes, and self-reported skills in medical students. METHODS: Third- and fourth-year medical students (n = 291) completed the module and a knowledge test. Of these students, 53 completed a pre- and postsurvey of confidence, attitudes, and self-reported skills and module evaluation. RESULTS: For the 291 students, knowledge scores increased significantly by 21.8 points (95% confidence interval [CI] = 19.7-23.8; p<0.001). The majority of scores on the survey items significantly increased postmodule, including: increase in confidence in assessing pain in pediatric patients (6% to 25%; p = 0.004), increase in responses of "strongly disagree" or "disagree" to the belief that opioids will delay diagnosis (62% to 85%; p = 0.005), and increase in responses of "frequently" or "very frequently" to "how often do you use behavioral instruments to assess pain severity?" (35% to 57%; p = 0.008). The majority reported they intend to make changes in behavior or practice (71%), and would recommend the module to fellow students (88%). CONCLUSION: This pilot program supports the effectiveness of a web-based module in improving knowledge, confidence, attitudes, and self-reported skills in PPM. Evaluation responses indicate high-quality content. Further evaluation for sustained impact is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".